Fast codeword search algorithm for ECVQ using hyperplane decision rule

Abstract

Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. Entropy-constrained vector quantization (ECVQ) codebook design based on empirical data involves an expensive training phase in which Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we describe a new method allowing significant acceleration in codebook design process. This method has feature of using a suitable hyperplane to partition the codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than previously known methods.

Fast Vector Quantization Encoding Algorithms for Image Compression

“…Many algorithms were proposed later in this field to reduce the computational calculation needed to accomplish the VQ technique. Some of these algorithms use one or more constraint inequality in the spatial domain to reduce the computational calculation needed to accomplish the VQ technique (Bake, Bae, & Sung, 1999;Cao, & Li 2000;Cardinal, 1999;Guan, & Kamel, 1992;Huang, Bi, Stiles, & Harris, 1992;Imamura, Swilem, & Hashimoto, 2003;Johnson, Ladner, & Riskin, 1996;Johnson, Ladner, & Riskin, 2000;Lai, & Lue, 1996;Lee, & Chen, 1994;Orchard, 1991;Swilem, Imamura, & Hashimoto, 2002;Wu, & Lin, 2000). Other algorithms exploit the topological structure of vectors to accomplish the same target (Lee, & Chen, 1995;Pan, Lu, & Sun, 2000;Song, & Ra, 2002;Swilem, Imamura, & Hashimoto, 2005).…”

A Fast Image Encoding Algorithm Based on the Pyramid Structure of Codewords

“…To release the computation burden, a lot of fast codeword search algorithms have been presented to speed up the searching process while maintaining the reconstructed performance same as that of the full search. These algorithms can be grouped into four categories: spatial inequality based (Bake, Jeon, & Sung, 1997;Bake, Bae, & Sung, 1999;Cao, & Li 2000;Cardinal, 1999;Guan, & Kamel, 1992;Huang, Bi, Stiles, & Harris, 1992;Imamura, Swilem, & Hashimoto, 2003;Johnson, Ladner, & Riskin, 1996;Johnson, Ladner, & Riskin, 2000;Lee, & Chen, 1994;Orchard, 1991;Swilem, Imamura, & Hashimoto, 2002;Wu, & Lin, 2000), pyramid structure based (Lee, & Chen, 1995;Pan, kotani & Ohmi, 2004;Song, & Ra, 2002;Swilem, Imamura, & Hashimoto, 2005), sub-vector based (Pan, Lu, & Sun, 2003;Pan, kotani & Ohmi, 2006;Shan, Fang, Weile & Tian, 2008), and transform domain based (Hwang, Jeng, & Chen, 1997;Jiang, Lu, & Wang, 2003;Chu, Lu, & Pan, 2007).…”

Fast VQ encoding algorithms using angular constraint

“…In this section, we introduce a technique using a hyperplane Il to divide the signal space into two half-spaces according to the vector feature. This method has been med with success in [7] for both Cardinal method and the double annulus method. This hyperplane H is used as a decision function that discriminates to which half-space a given vector x belongs by the following conditions: …”

“…To avoid such an exhaustive search through the codebook, many fast algorithms [3]- [7] have been proposed. These algorithms reduce the computational complexity by performing some simple tests before computing the distortion between the input vector and each codeword.…”